v1.0

distilabel-internal-testing/instructions/README.md

+---
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 3872
+    num_examples: 10
+  download_size: 3832
+  dataset_size: 3872
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+---

distilabel/.pre-commit-config.yaml

+repos:
+  - repo: https://github.com/Lucas-C/pre-commit-hooks
+    rev: v1.5.5
+    hooks:
+      - id: insert-license
+        name: "Insert license header in Python source files"
+        files: \.py$
+        args:
+          - --license-filepath
+          - LICENSE_HEADER
+          - --fuzzy-match-generates-todo
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.1
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format
+
+ci:
+  autofix_commit_msg: |
+    [pre-commit.ci] auto fixes from pre-commit.com hooks
+    for more information, see https://pre-commit.ci
+  autofix_prs: true
+  autoupdate_branch: ""
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit autoupdate"
+  autoupdate_schedule: weekly
+  skip: []
+  submodules: false

distilabel/CITATION.cff

+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Bartolomé"
+  given-names: "Álvaro"
+- family-names: "Martín-Blázquez"
+  given-names: "Gabriel"
+- family-names: "Piqueres-Lajarín"
+  given-names: "Agustín"
+- family-names: "Vila-Suero"
+  given-names: "Daniel"
+title: "Distilabel: An AI Feedback (AIF) framework for building datasets with and for LLMs."
+version: 1.1.1
+date-released: 2024-05-22
+url: "https://github.com/argilla-io/distilabel"

distilabel/LICENSE

+                                 Apache License
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+   Copyright [2023-present] [Argilla, Inc.]
+
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distilabel/LICENSE_HEADER

+Copyright 2023-present, Argilla, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
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+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
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+See the License for the specific language governing permissions and
+limitations under the License.

distilabel/Makefile

+sources = src/distilabel tests
+
+.PHONY: format
+format:
+	ruff --version
+	ruff check --fix $(sources)
+	ruff format $(sources)
+
+.PHONY: lint
+lint:
+	ruff --version
+	ruff check $(sources)
+	ruff format --check $(sources)
+
+.PHONY: unit-tests
+unit-tests:
+	pytest tests/unit
+
+.PHONY: integration-tests
+integration-tests:
+	pytest tests/integration

distilabel/README.md

+> [!IMPORTANT]  
+The original authors have moved on to other projects. While the code might still be functional for its original purpose, please be aware that the original team does not plan to develop new features, bug fixes, or updates. If you'd like to become a maintainer, please open an issue to discuss it.
+> 
+<div align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://github.com/argilla-io/distilabel/blob/main/docs/assets/distilabel-white.png?raw=true">
+    <img alt="Distilabel Logo" src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-black.png">
+  </picture>
+</div>
+
+<h3 align="center">Synthesize data for AI and add feedback on the fly!</h3>
+
+<p align="center">
+  <a  href="https://pypi.org/project/distilabel/">
+    <img alt="CI" src="https://img.shields.io/pypi/v/distilabel.svg?style=flat-round&logo=pypi&logoColor=white">
+  </a>
+  <a href="https://pepy.tech/project/distilabel">
+    <img alt="CI" src="https://static.pepy.tech/personalized-badge/distilabel?period=month&units=international_system&left_color=grey&right_color=blue&left_text=pypi%20downloads/month">
+  </a>
+</p>
+
+<p align="center">
+  <a href="https://twitter.com/argilla_io">
+    <img src="https://img.shields.io/badge/twitter-black?logo=x"/>
+  </a>
+  <a href="https://www.linkedin.com/company/argilla-io">
+    <img src="https://img.shields.io/badge/linkedin-blue?logo=linkedin"/>
+  </a>
+  <a href="http://hf.co/join/discord">
+  <img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
+  </a>
+</p>
+
+Distilabel is the framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
+
+If you just want to get started, we recommend you check the [documentation](http://distilabel.argilla.io/). Curious, and want to know more? Keep reading!
+<!-- ![overview](https://github.com/argilla-io/distilabel/assets/36760800/360110da-809d-4e24-a29b-1a1a8bc4f9b7)  -->
+
+## Why use distilabel?
+
+Distilabel can be used for generating synthetic data and AI feedback for a wide variety of projects including traditional predictive NLP (classification, extraction, etc.), or generative and large language model scenarios (instruction following, dialogue generation, judging etc.). Distilabel's programmatic approach allows you to build scalable pipelines for data generation and AI feedback. The goal of distilabel is to accelerate your AI development by quickly generating high-quality, diverse datasets based on verified research methodologies for generating and judging with AI feedback.
+
+### Improve your AI output quality through data quality
+
+Compute is expensive and output quality is important. We help you **focus on data quality**, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time **achieving and keeping high-quality standards for your data**.
+
+### Take control of your data and models
+
+**Ownership of data for fine-tuning your own LLMs** is not easy but Distilabel can help you to get started. We integrate **AI feedback from any LLM provider out there** using one unified API.
+
+### Improve efficiency by quickly iterating on the right research and LLMs
+
+Synthesize and judge data with **latest research papers** while ensuring **flexibility, scalability and fault tolerance**. So you can focus on improving your data and training your models.
+
+## Community
+
+We are an open-source community-driven project and we love to hear from you. Here are some ways to get involved:
+
+- [Community Meetup](https://lu.ma/embed-checkout/evt-IQtRiSuXZCIW6FB): listen in or present during one of our bi-weekly events.
+
+- [Discord](http://hf.co/join/discord): get direct support from the community in #argilla-general and #argilla-help.
+
+- [Roadmap](https://github.com/orgs/argilla-io/projects/10/views/1): plans change but we love to discuss those with our community so feel encouraged to participate.
+
+## What do people build with Distilabel?
+
+The Argilla community uses distilabel to create amazing [datasets](https://huggingface.co/datasets?other=distilabel) and [models](https://huggingface.co/models?other=distilabel).
+
+- The [1M OpenHermesPreference](https://huggingface.co/datasets/argilla/OpenHermesPreferences) is a dataset of ~1 million AI preferences derived from teknium/OpenHermes-2.5. It shows how we can use Distilabel to **synthesize data on an immense scale**.
+- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B), show how we **improve model performance by filtering out 50%** of the original dataset through **AI feedback**.
+- The [haiku DPO data](https://github.com/davanstrien/haiku-dpo) outlines how anyone can create a **dataset for a specific task** and **the latest research papers** to improve the quality of the dataset.
+
+## Installation
+
+```sh
+pip install distilabel --upgrade
+```
+
+Requires Python 3.9+
+
+In addition, the following extras are available:
+
+### LLMs
+
+- `anthropic`: for using models available in [Anthropic API](https://www.anthropic.com/api) via the `AnthropicLLM` integration.
+- `cohere`: for using models available in [Cohere](https://cohere.ai/) via the `CohereLLM` integration.
+- `argilla`: for exporting the generated datasets to [Argilla](https://argilla.io/).
+- `groq`: for using models available in [Groq](https://groq.com/) using [`groq`](https://github.com/groq/groq-python) Python client via the `GroqLLM` integration.
+- `hf-inference-endpoints`: for using the [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) via the `InferenceEndpointsLLM` integration.
+- `hf-transformers`: for using models available in [transformers](https://github.com/huggingface/transformers) package via the `TransformersLLM` integration.
+- `litellm`: for using [`LiteLLM`](https://github.com/BerriAI/litellm) to call any LLM using OpenAI format via the `LiteLLM` integration.
+- `llama-cpp`: for using [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) Python bindings for `llama.cpp` via the `LlamaCppLLM` integration.
+- `mistralai`: for using models available in [Mistral AI API](https://mistral.ai/news/la-plateforme/) via the `MistralAILLM` integration.
+- `ollama`: for using [Ollama](https://ollama.com/) and their available models via `OllamaLLM` integration.
+- `openai`: for using [OpenAI API](https://openai.com/blog/openai-api) models via the `OpenAILLM` integration, or the rest of the integrations based on OpenAI and relying on its client as `AnyscaleLLM`, `AzureOpenAILLM`, and `TogetherLLM`.
+- `vertexai`: for using [Google Vertex AI](https://cloud.google.com/vertex-ai) proprietary models via the `VertexAILLM` integration.
+- `vllm`: for using [vllm](https://github.com/vllm-project/vllm) serving engine via the `vLLM` integration.
+- `sentence-transformers`: for generating sentence embeddings using [sentence-transformers](https://github.com/UKPLab/sentence-transformers).
+- `mlx`: for using [MLX](https://github.com/ml-explore/mlx) models via the `MlxLLM` integration.
+
+### Structured generation
+
+- `outlines`: for using structured generation of LLMs with [outlines](https://github.com/outlines-dev/outlines).
+- `instructor`: for using structured generation of LLMs with [Instructor](https://github.com/jxnl/instructor/).
+
+### Data processing
+
+- `ray`: for scaling and distributing a pipeline with [Ray](https://github.com/ray-project/ray).
+- `faiss-cpu` and `faiss-gpu`: for generating sentence embeddings using [faiss](https://github.com/facebookresearch/faiss).
+- `text-clustering`: for using text clustering with [UMAP](https://github.com/lmcinnes/umap) and [Scikit-learn](https://github.com/scikit-learn/scikit-learn).
+- `minhash`: for using minhash for duplicate detection with [datasketch](https://github.com/datasketch/datasketch) and [nltk](https://github.com/nltk/nltk).
+
+### Example
+
+To run the following example you must install `distilabel` with the `hf-inference-endpoints` extra:
+
+```sh
+pip install "distilabel[hf-inference-endpoints]" --upgrade
+```
+
+Then run:
+
+```python
+from datasets import load_dataset
+
+from distilabel.models import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    TextGeneration(
+        llm=InferenceEndpointsLLM(
+            model_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
+            generation_kwargs={"temperature": 0.7, "max_new_tokens": 512},
+        ),
+    )
+
+if __name__ == "__main__":
+    dataset = load_dataset("distilabel-internal-testing/instructions", split="test")
+    distiset = pipeline.run(dataset=dataset)
+    distiset.push_to_hub(repo_id="distilabel-example")
+```
+
+## Badges
+
+If you build something cool with `distilabel` consider adding one of these badges to your dataset or model card.
+
+    [<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
+
+[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
+
+    [<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
+
+[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
+
+## Contribute
+
+To directly contribute with `distilabel`, check our [good first issues](https://github.com/argilla-io/distilabel/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or [open a new one](https://github.com/argilla-io/distilabel/issues/new/choose).
+
+## Citation
+
+```bibtex
+@misc{distilabel-argilla-2024,
+  author = {Álvaro Bartolomé Del Canto and Gabriel Martín Blázquez and Agustín Piqueres Lajarín and Daniel Vila Suero},
+  title = {Distilabel: An AI Feedback (AIF) framework for building datasets with and for LLMs},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/argilla-io/distilabel}}
+}
+```

distilabel/docs/CNAME

+distilabel.argilla.io

distilabel/docs/api/cli.md

+# Command Line Interface (CLI)
+
+This section contains the API reference for the CLI. For more information on how to use the CLI, see [Tutorial - CLI](../sections/how_to_guides/advanced/cli/index.md).
+
+## Utility functions for the `distilabel pipeline` sub-commands
+
+Here are some utility functions to help working with the pipelines in the console.
+
+:::distilabel.cli.pipeline.utils

distilabel/docs/api/distiset.md

+# Distiset
+
+This section contains the API reference for the Distiset. For more information on how to use the CLI, see [Tutorial - CLI](../sections/how_to_guides/advanced/distiset.md).
+
+:::distilabel.distiset.Distiset
+:::distilabel.distiset.create_distiset

distilabel/docs/api/errors.md

+# Errors
+
+This section contains the `distilabel` custom errors. Unlike [exceptions](exceptions.md), errors in `distilabel` are used to handle unexpected situations that can't be anticipated and that can't be handled in a controlled way.
+
+:::distilabel.errors.DistilabelError
+:::distilabel.errors.DistilabelUserError
+:::distilabel.errors.DistilabelTypeError
+:::distilabel.errors.DistilabelNotImplementedError

distilabel/docs/api/exceptions.md

+# Exceptions
+
+This section contains the `distilabel` custom exceptions. Unlike [errors](errors.md), exceptions in `distilabel` are used to handle specific situations that can be anticipated and that can be handled in a controlled way internally by the library.
+
+:::distilabel.exceptions.DistilabelException
+:::distilabel.exceptions.DistilabelGenerationException
+:::distilabel.exceptions.DistilabelOfflineBatchGenerationNotFinishedException

distilabel/docs/api/mixins/requirements.md

+::: distilabel.mixins.requirements.RequirementsMixin

distilabel/docs/api/mixins/runtime_parameters.md

+::: distilabel.mixins.runtime_parameters.RuntimeParametersMixin

distilabel/docs/api/models/embedding/embedding_gallery.md

+# Embedding Gallery
+
+This section contains the existing [`Embeddings`][distilabel.models.embeddings] subclasses implemented in `distilabel`.
+
+::: distilabel.models.embeddings
+    options:
+        filters:
+        - "!^Embeddings$"
\ No newline at end of file

distilabel/docs/api/models/embedding/index.md

+# Embedding
+
+This section contains the API reference for the `distilabel` embeddings.
+
+For more information on how the [`Embeddings`][distilabel.steps.tasks.Task] works and see some examples.
+
+::: distilabel.models.embeddings.base
\ No newline at end of file

distilabel/docs/api/models/image_generation/image_generation_gallery.md

+# ImageGenerationModel Gallery
+
+This section contains the existing [`ImageGenerationModel`][distilabel.models.image_generation] subclasses implemented in `distilabel`.
+
+::: distilabel.models.image_generation
+    options:
+        filters:
+        - "!^ImageGenerationModel$"
+        - "!^AsyngImageGenerationModel$"
+        - "!typing"
\ No newline at end of file

distilabel/docs/api/models/image_generation/index.md

+# ImageGenerationModel
+
+This section contains the API reference for the `distilabel` image generation models, both for the [`ImageGenerationModel`][distilabel.models.image_generation.ImageGenerationModel] synchronous implementation, and for the [`AsyncImageGenerationModel`][distilabel.models.image_generation.AsyncImageGenerationModel] asynchronous one.
+
+For more information and examples on how to use existing LLMs or create custom ones, please refer to [Tutorial - ImageGenerationModel](../../../sections/how_to_guides/basic/task/image_task.md).
+
+::: distilabel.models.image_generation.base

distilabel/docs/api/models/llm/index.md

+# LLM
+
+This section contains the API reference for the `distilabel` LLMs, both for the [`LLM`][distilabel.models.llms.LLM] synchronous implementation, and for the [`AsyncLLM`][distilabel.models.llms.AsyncLLM] asynchronous one.
+
+For more information and examples on how to use existing LLMs or create custom ones, please refer to [Tutorial - LLM](../../../sections/how_to_guides/basic/llm/index.md).
+
+::: distilabel.models.llms.base